Search Results for author:
Found 40 papers, 14 papers with code
Affectively Framework: Towards Human-like Affect-Based Agents
Game environments offer a unique opportunity for training virtual agents due to their interactive nature, which provides diverse play traces and affect labels.
Evolutionary Machine Learning and Games
Evolutionary machine learning (EML) has been applied to games in multiple ways, and for multiple different purposes.
A Preliminary Study on a Conceptual Game Feature Generation and Recommendation System
This paper introduces a system used to generate game feature suggestions based on a text prompt.
Lode Enhancer: Level Co-creation Through Scaling
We explore AI-powered upscaling as a design assistance tool in the context of creating 2D game levels.
Lode Encoder: AI-constrained co-creativity
We present Lode Encoder, a gamified mixed-initiative level creation system for the classic platform-puzzle game Lode Runner.
Controllable Path of Destruction
The PoD method is very data-efficient in terms of original training examples and well-suited to functional artifacts composed of categorical data, such as game levels and discrete 3D structures.
Generative Personas That Behave and Experience Like Humans
Using artificial intelligence (AI) to automatically test a game remains a critical challenge for the development of richer and more complex game worlds and for the advancement of AI at large.
Play with Emotion: Affect-Driven Reinforcement Learning
According to the proposed paradigm, RL agents learn a policy (i. e. affective interaction) by attempting to maximize a set of rewards (i. e. behavioral and affective patterns) via their experience with their environment (i. e. context).
Mutation Models: Learning to Generate Levels by Imitating Evolution
Search-based procedural content generation (PCG) is a well-known method for level generation in games.
Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials
In this paper, we present a method for automated persona-driven video game tutorial level generation.
Predicting Personas Using Mechanic Frequencies and Game State Traces
We investigate how to efficiently predict play personas based on playtraces.
Path of Destruction: Learning an Iterative Level Generator Using a Small Dataset
The Path of Destruction method, as we call it, views level generation as repair; levels are created by iteratively repairing from a random starting level.
Learning Controllable Content Generators
It has recently been shown that reinforcement learning can be used to train generators capable of producing high-quality game levels, with quality defined in terms of some user-specified heuristic.
Game Mechanic Alignment Theory and Discovery
We present a new concept called Game Mechanic Alignment theory as a way to organize game mechanics through the lens of systemic rewards and agential motivations.
Deep Learning for Procedural Content Generation
This article surveys the various deep learning methods that have been applied to generate game content directly or indirectly, discusses deep learning methods that could be used for content generation purposes but are rarely used today, and envisages some limitations and potential future directions of deep learning for procedural content generation.
Mixed-Initiative Level Design with RL Brush
This paper introduces RL Brush, a level-editing tool for tile-based games designed for mixed-initiative co-creation.
Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network
Generative adversarial networks (GANs) are quickly becoming a ubiquitous approach to procedurally generating video game levels.
Multi-Objective level generator generation with Marahel
This paper introduces a new system to design constructive level generators by searching the space of constructive level generators defined by Marahel language.
Mech-Elites: Illuminating the Mechanic Space of GVGAI
This paper introduces a fully automatic method of mechanic illumination for general video game level generation.
Mario Level Generation From Mechanics Using Scene Stitching
This paper presents a level generation method for Super Mario by stitching together pre-generated "scenes" that contain specific mechanics, using mechanic-sequences from agent playthroughs as input specifications.
Rotation, Translation, and Cropping for Zero-Shot Generalization
Deep Reinforcement Learning (DRL) has shown impressive performance on domains with visual inputs, in particular various games.
PCGRL: Procedural Content Generation via Reinforcement Learning
We investigate how reinforcement learning can be used to train level-designing agents.
Bootstrapping Conditional GANs for Video Game Level Generation
Theresults demonstrate that the new approach does not only gen-erate a larger number of levels that are playable but also gen-erates fewer duplicate levels compared to a standard GAN.
Automatic Critical Mechanic Discovery Using Playtraces in Video Games
In a user study, human-identified mechanics are compared against system-identified critical mechanics to verify alignment between humans and the system.
Superstition in the Network: Deep Reinforcement Learning Plays Deceptive Games
Deep reinforcement learning has learned to play many games well, but failed on others.
Procedural Content Generation through Quality Diversity
Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics.
General Video Game Rule Generation
We introduce the General Video Game Rule Generation problem, and the eponymous software framework which will be used in a new track of the General Video Game AI (GVGAI) competition.
Two-step Constructive Approaches for Dungeon Generation
This paper presents a two-step generative approach for creating dungeons in the rogue-like puzzle game MiniDungeons 2.
ELIMINATION from Design to Analysis
Elimination is a word puzzle game for browsers and mobile devices, where all levels are generated by a constrained evolutionary algorithm with no human intervention.
Intentional Computational Level Design
The procedural generation of levels and content in video games is a challenging AI problem.
Tree Search vs Optimization Approaches for Map Generation
We compare them on three different game level generation problems: Binary, Zelda, and Sokoban.
Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning
Unlike other benchmarks such as the Arcade Learning Environment, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment.
A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking
This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms.
Generating Levels That Teach Mechanics
The automatic generation of game tutorials is a challenging AI problem.
AtDelfi: Automatically Designing Legible, Full Instructions For Games
This paper introduces a fully automatic method for generating video game tutorials.
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation
However, when neural networks are trained in a fixed environment, such as a single level in a video game, they will usually overfit and fail to generalize to new levels.
Talakat: Bullet Hell Generation through Constrained Map-Elites
We describe a search-based approach to generating new levels for bullet hell games, which are action games characterized by and requiring avoidance of a very large amount of projectiles.
"Press Space to Fire": Automatic Video Game Tutorial Generation
We propose the problem of tutorial generation for games, i. e. to generate tutorials which can teach players to play games, as an AI problem.
General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms
In 2014, The General Video Game AI (GVGAI) competition framework was created and released with the purpose of providing researchers a common open-source and easy to use platform for testing their AI methods with potentially infinity of games created using Video Game Description Language (VGDL).
DeepTingle
DeepTingle is a text prediction and classification system trained on the collected works of the renowned fantastic gay erotica author Chuck Tingle.
